Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditions
Magnesium (Mg) alloys have found potential applications in aeronautical, automotive, 3C industries, and the like owing to their good machinability, high specific strength, and low density. However, one of the main obstacles in impeding the Mg is weak corrosion resistance. Herein, the corrosion behav...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024001506 |
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| author | Uzair Sajjad Aqeel Abbas Imtiyaz Hussain Muhammad Sultan Hafiz Muhammad Ali Wei-Mon Yan |
| author_facet | Uzair Sajjad Aqeel Abbas Imtiyaz Hussain Muhammad Sultan Hafiz Muhammad Ali Wei-Mon Yan |
| author_sort | Uzair Sajjad |
| collection | DOAJ |
| description | Magnesium (Mg) alloys have found potential applications in aeronautical, automotive, 3C industries, and the like owing to their good machinability, high specific strength, and low density. However, one of the main obstacles in impeding the Mg is weak corrosion resistance. Herein, the corrosion behavior of WS2/AZ91 composites and the effect of severe deformation through equal channel angular pressing was investigated experimentally and analytically via three-electrode system in a 3.5 wt% NaCl solution and data driven modelling. The experimental data of the current density and corrosion potentials of different composites at different deformation conditions was first correlated by Pearson, Spearman, and Kendall correlations. After that Bayesian surrogate Gaussian process (GP) assisted optimal neural network was developed to assess the corrosion behavior of different metal matrix composites at different deformation conditions. The correlation matrix showed that for different weight concentrations such as 0 wt %, 0.6 wt %, and 1 wt %, the Pearson correlation value becomes 0.77, 0.64, and 0.7, respectively. Similar to the Pearson correlation, the Kendall and Spearman correlations also showed relatively higher values for 0 wt % and 1 wt % compared to 0.6 wt % concentration. The proposed neural network model expressed a great accuracy in terms of correlation coefficient (R2 = 0.9668), mean absolute error (MAE = 0.0583), mean square error (MSE = 0.0405), and mean absolute percentage error (MAPE = 2.183). Although different concentrations and deformation conditions were included in the data, yet, the proposed DNN model was able to predict the current density data with a great accuracy. Finally, the explainable artificial intelligence was used to interpret the prediction of the developed model for different deformations and composite concentrations. |
| first_indexed | 2024-03-07T23:59:26Z |
| format | Article |
| id | doaj.art-09d3d861389541e8bdc86bf36ca884b8 |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-24T20:02:15Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-09d3d861389541e8bdc86bf36ca884b82024-03-24T07:01:06ZengElsevierResults in Engineering2590-12302024-03-0121101897Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditionsUzair Sajjad0Aqeel Abbas1Imtiyaz Hussain2Muhammad Sultan3Hafiz Muhammad Ali4Wei-Mon Yan5Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan; Corresponding author.Department of Mechanical Engineering, NFC Institute of Engineering and Fertilizer Research Faisalabad, Paksitan; Corresponding author.Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei, 10608, TaiwanDepartment of Agricultural Engineering, Bahauddin Zakariya University, Multan, 60800, PakistanMechanical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi ArabiaDepartment of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan; Corresponding author.Magnesium (Mg) alloys have found potential applications in aeronautical, automotive, 3C industries, and the like owing to their good machinability, high specific strength, and low density. However, one of the main obstacles in impeding the Mg is weak corrosion resistance. Herein, the corrosion behavior of WS2/AZ91 composites and the effect of severe deformation through equal channel angular pressing was investigated experimentally and analytically via three-electrode system in a 3.5 wt% NaCl solution and data driven modelling. The experimental data of the current density and corrosion potentials of different composites at different deformation conditions was first correlated by Pearson, Spearman, and Kendall correlations. After that Bayesian surrogate Gaussian process (GP) assisted optimal neural network was developed to assess the corrosion behavior of different metal matrix composites at different deformation conditions. The correlation matrix showed that for different weight concentrations such as 0 wt %, 0.6 wt %, and 1 wt %, the Pearson correlation value becomes 0.77, 0.64, and 0.7, respectively. Similar to the Pearson correlation, the Kendall and Spearman correlations also showed relatively higher values for 0 wt % and 1 wt % compared to 0.6 wt % concentration. The proposed neural network model expressed a great accuracy in terms of correlation coefficient (R2 = 0.9668), mean absolute error (MAE = 0.0583), mean square error (MSE = 0.0405), and mean absolute percentage error (MAPE = 2.183). Although different concentrations and deformation conditions were included in the data, yet, the proposed DNN model was able to predict the current density data with a great accuracy. Finally, the explainable artificial intelligence was used to interpret the prediction of the developed model for different deformations and composite concentrations.http://www.sciencedirect.com/science/article/pii/S2590123024001506Metal matrix compositesMagnesium alloyEqual angular channel pressingCorrosion potentialCorrelation |
| spellingShingle | Uzair Sajjad Aqeel Abbas Imtiyaz Hussain Muhammad Sultan Hafiz Muhammad Ali Wei-Mon Yan Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditions Results in Engineering Metal matrix composites Magnesium alloy Equal angular channel pressing Corrosion potential Correlation |
| title | Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditions |
| title_full | Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditions |
| title_fullStr | Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditions |
| title_full_unstemmed | Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditions |
| title_short | Explainable artificial intelligence-based insights into the corrosion behavior of WS2/AZ91 composites subjected to severe deformation conditions |
| title_sort | explainable artificial intelligence based insights into the corrosion behavior of ws2 az91 composites subjected to severe deformation conditions |
| topic | Metal matrix composites Magnesium alloy Equal angular channel pressing Corrosion potential Correlation |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024001506 |
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